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Applied Mathematics Versus Statistics for Data Science research

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I recently applied to graduate school in both Statistics and Applied Mathematics and have been accepted to both types of programs for a Master's (hoping to continue on to a PhD). I often see people tying Machine Learning, Data Science, etc together with Statistics, but have also seen these areas mentioned in Applied Mathematics programs which do not include Statistics as a part of the department. I was curious how one might differentiate the careers for Applied Mathematicians versus Statisticians in these particular areas and how the research in Statistics and Applied Math on ML and Data Science is different?



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Did you apply to NYU's MS program in Data Science though? Applied Mathematics is a VERY broad field, ranging from extremely pure math (PDE, Harmonic Analysis) to extremely applied (measure theory, probability, statistics with main focus into solving real-world problems). Statistics is a branch of applied math, but the hardest statistics class in grad school is mostly in measure theory/probability theory/mathematical statistics. So if you want to be able to understand EVERYTHING that involves the mathy part in statistics AND to develop new optimization models, go with applied math. If you just want to deal with big data without really understanding anything involving developing NEW models, having a strong statistics background is more helpful. 

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